As more healthcare organizations participate in value-based and risk-adjusted payment arrangements, attention is increasingly turning to whether clinically supported conditions are accurately documented and captured for risk adjustment. RapidClaims says its revenue cycle automation platform is increasingly being applied to this problem, using AI medical coding and real-time clinical documentation improvement to help providers capture diagnoses that risk-adjustment models depend on but that traditional coding workflows frequently miss.
The scale of the gap is significant. Industry research has identified significant gaps between conditions documented in clinical records and diagnoses ultimately captured for risk adjustment. Clinicians and coding teams can also face challenges understanding which documented conditions qualify for capture under changing HCC methodologies. For organizations participating in risk-adjusted arrangements, incomplete diagnosis capture can result in understated risk scores and potentially reduce risk-adjusted reimbursement or shared-savings opportunities, depending on the payment model.
Unlike a conventional claim denial, a missed risk-adjustment diagnosis may not generate an immediate rejection or denial, making documentation and diagnosis-capture gaps harder to identify through traditional denial reports. RapidClaims says it is increasingly applying its platform to value-based care and risk-adjustment workflows, as revenue cycle leaders look for ways to catch these gaps prospectively rather than discovering them months later.
RapidCDI provides point-of-care documentation prompts tied to clinical information in the patient’s record, allowing clinicians to review potential documentation and coding opportunities within their existing workflow. Each prompt is tied to the specific lab result, medication, or clinical note that triggered the suggestion, allowing physicians to quickly confirm or reject a recommended diagnosis capture without leaving their existing workflow. RapidClaims reports a 24% improvement in HCC capture with RapidCDI’s point-of-care risk-adjustment optimization. RapidClaims also cites a customer report that physicians saved an average of 30 minutes per day through AI documentation suggestions.
This capability sits alongside the rest of RapidClaims’ revenue cycle management software, which spans AI medical coding, automated claims scrubbing, and denial resolution. For organizations managing both fee-for-service and value-based contracts, RapidClaims positions the unified platform as a way to bring standard claims coding and risk-adjustment workflows into the same technology environment.
The timing reflects a broader industry shift. As CMS completes the phase-in of its updated V28 CMS-HCC risk-adjustment model for most Medicare Advantage organizations in 2026, healthcare organizations are under growing pressure to ensure that documentation and coding workflows keep pace with the revised methodology. For organizations participating in risk-adjusted payment arrangements, incomplete or unsupported diagnosis capture can affect risk scores and reimbursement, making accurate documentation an important part of revenue-cycle and value-based-care operations.
For health systems evaluating revenue cycle management automation with value-based care in mind, the lesson from RapidClaims’ experience is that risk-adjustment coding accuracy can’t be treated as a separate initiative from standard claims coding. Both ultimately depend on the same underlying documentation quality, and platforms that address both simultaneously – rather than requiring separate tools for fee-for-service claims and HCC capture – are increasingly seen as a more sustainable path for organizations managing an expanding mix of value-based contracts. As value-based care continues to grow as a share of U.S. healthcare payment, RapidClaims expects demand for this kind of connected, risk-adjustment-aware revenue cycle automation to keep expanding through the rest of 2026.
More broadly, RapidClaims says its experience in this space points to a shift in how health systems are approaching AI RCM software evaluations. Organizations that once treated fee-for-service coding accuracy and value-based risk-adjustment capture as separate workstreams, often managed by different vendors or internal teams, are increasingly consolidating both onto a single platform. For revenue cycle leaders trying to manage a growing mix of payment models with limited coding staff, that consolidation can become increasingly attractive for organizations managing multiple payment models with limited coding resources.